Analysis of the effect of electrical vehicles charging load on utility grid based on the residential behaviour characteristics

• Main Authors: Liu, Manjia, Bi, Wei, Liu, Ming, Jin, Chen, Xiong, Haozhe, Li, Ning, Qin, Hanshi, Hou, Youmin
• Format: Conference Proceeding
• Language: English
• Subjects: Behavioral sciences; Clustering algorithms; Electric vehicle; Electric vehicle charging; GIS system; K-means clustering algorithm; Renewable energy; Renewable energy sources; Solar energy; Transportation; Wind energy
• ISSN: 2768-0088
• DOI: 10.1109/ICSGSC59580.2023.10319149

Electric vehicles (EVs) are regarded as a particular energy storage device in the electric grid and one of the green urban transportation approaches powered by electric energy to reduce urban carbon emissions. It has a strong impact on the charging behavior of urban electric load and electric load systems. Therefore, an urgent need is to analyze the characteristics of current urban electric vehicle charging behavior and its overall impact on urban power operations. In this paper, the K-means clustering algorithm and GIS system are utilized to analyze urban electric vehicle users' spatial and temporal characteristics of charging behavior. The paper exemplifies a province in China during the period from March 2022 to May 2023. The results show that the charging behaviors present an obvious spatiotemporal heterogeneity. The characteristics show different charging load peaks ranging from 1 to 3 throughout the day. This paper further compares the characteristics of residential EV charging behavior, the provincial electricity load and provincial renewable energy generation power. It provides a reference for the government to meet the peaks and summers of the electric power system, to reduce the rate of renewable energy wind and solar energy abandonment, and to reduce the peaks and valleys of electric power.